new introplot{

code/chirpdetection.py

@@ -262,14 +262,14 @@ class ChirpPlotBuffer:
             # facecolors="none",
         )
 
-        ax0.set_ylabel("frequency [Hz]")
+        ax0.set_ylabel("Frequency [Hz]")
         ax1.set_ylabel(r"$\mu$V")
         ax2.set_ylabel(r"$\mu$V")
         ax3.set_ylabel("Hz")
         ax4.set_ylabel(r"$\mu$V")
         ax5.set_ylabel(r"$\mu$V")
         ax6.set_ylabel("Hz")
-        ax6.set_xlabel("time [s]")
+        ax6.set_xlabel("Time [s]")
 
         plt.setp(ax0.get_xticklabels(), visible=False)
         plt.setp(ax1.get_xticklabels(), visible=False)
@@ -652,16 +652,16 @@ def chirpdetection(datapath: str, plot: str, debug: str = 'false') -> None:
     raw_time = np.arange(data.raw.shape[0]) / data.raw_rate
 
     # good chirp times for data: 2022-06-02-10_00
-    # window_start_index = (3 * 60 * 60 + 6 * 60 + 43.5) * data.raw_rate
-    # window_duration_index = 60 * data.raw_rate
+    window_start_index = (3 * 60 * 60 + 6 * 60 + 43.5) * data.raw_rate
+    window_duration_index = 60 * data.raw_rate
 
     #     t0 = 0
     #     dt = data.raw.shape[0]
     # window_start_seconds = (23495 + ((28336-23495)/3)) * data.raw_rate
     # window_duration_seconds = (28336 - 23495) * data.raw_rate
 
-    window_start_index = 0
-    window_duration_index = data.raw.shape[0]
+    # window_start_index = 0
+    # window_duration_index = data.raw.shape[0]
 
     # generate starting points of rolling window
     window_start_indices = np.arange(
@@ -675,7 +675,7 @@ def chirpdetection(datapath: str, plot: str, debug: str = 'false') -> None:
     multiwindow_chirps = []
     multiwindow_ids = []
 
-    for st, window_start_index in enumerate(window_start_indices[3175:]):
+    for st, window_start_index in enumerate(window_start_indices):
 
         logger.info(f"Processing window {st+1} of {len(window_start_indices)}")
 

code/modules/plotstyle.py

@@ -33,6 +33,7 @@ def PlotStyle() -> None:
         gblue1 = "#89b4fa"
         gblue2 = "#89dceb"
         gblue3 = "#a6e3a1"
+        g = "#76a0fa"
 
         @classmethod
         def lims(cls, track1, track2):
@@ -112,7 +113,6 @@ def PlotStyle() -> None:
             plt.setp(bp["caps"], color=white)
             plt.setp(bp["medians"], color=black)
 
-
         @classmethod
         def label_subplots(cls, labels, axes, fig):
             for axis, label in zip(axes, labels):

code/plot_introduction_specs.py

@@ -41,10 +41,10 @@ def main():
     freqtime2, freq2 = instantaneous_frequency(
         filtered2, data.raw_rate, smoothing_window=3)
 
-    ax.plot(freqtime1*timescaler, freq1, color=ps.red,
-            lw=2, label="fish 1")
-    ax.plot(freqtime2*timescaler, freq2, color=ps.orange,
-            lw=2, label="fish 2")
+    ax.plot(freqtime1*timescaler, freq1, color=ps.g,
+            lw=2, label="Fish 1")
+    ax.plot(freqtime2*timescaler, freq2, color=ps.gray,
+            lw=2, label="Fish 2")
     ax.legend(bbox_to_anchor=(0, 1.02, 1, 0.2), loc="lower center",
               mode="normal", borderaxespad=0, ncol=2)
     # ax.legend(bbox_to_anchor=(1.04, 1), borderaxespad=0)
@@ -74,8 +74,8 @@ def main():
         origin="lower",
         interpolation="gaussian",
         alpha=1,
-        vmin=-100,
-        vmax=-80,
+        # vmin=-100,
+        # vmax=-80,
     )
     # ps.hide_xax(ax2)
 
@@ -103,8 +103,8 @@ def main():
     # )
     # # ps.hide_xax(ax3)
 
-    ax.set_xlabel("time [ms]")
-    ax.set_ylabel("frequency [Hz]")
+    ax.set_xlabel("Time [ms]")
+    ax.set_ylabel("Frequency [Hz]")
 
     # ax.set_yticks(np.arange(400, 1201, 400))
     # ax.spines.left.set_bounds((400, 1200))

code/plot_kdes.py

@@ -287,14 +287,14 @@ def main(dataroot):
             ax[i].plot(kde_time, np.median(loser_offsets_boot[-1], axis=0),
                        color=ps.black, linewidth=2)
 
-            ax[i].fill_between(
-                kde_time,
-                np.percentile(loser_offsets_jackknife, 5, axis=0),
-                np.percentile(loser_offsets_jackknife, 95, axis=0),
-                color=ps.blue,
-                alpha=0.5)
-            ax[i].plot(kde_time, np.median(loser_offsets_jackknife, axis=0),
-                       color=ps.white, linewidth=2)
+            # ax[i].fill_between(
+            #     kde_time,
+            #     np.percentile(loser_offsets_jackknife, 5, axis=0),
+            #     np.percentile(loser_offsets_jackknife, 95, axis=0),
+            #     color=ps.blue,
+            #     alpha=0.5)
+            # ax[i].plot(kde_time, np.median(loser_offsets_jackknife, axis=0),
+            #            color=ps.white, linewidth=2)
 
             ax[i].set_xlim(-60, 60)